Predicting life expectancy#
# TBA: header image
It is no secret that life expectancy has been increasing rapidly over the past couple of decades. A crucial indicator of a nation’s health and well-being can be traced back to its life expectancy statistic. This statistic is influenced by a multitude of factors, such as: economic stability, healthcare quality lifestyle, education, environmental conditions and many more. The question begs however, which of these factors contribute the most to a nation’s life expectancy? One might argue that only education plays a role, because all other factors are dependent on it. Another person might argue that not all of these factors are dependent on a nation’s level of education, thus its impact might not be as significant as one expects. This project aims to put these two perspectives to the test, by analyzing several key factors contributing to a nation’s life expectancy.
Several datasets about factors related to life expectancy are used in this project. Using sophisticated modeling techniques and visualization, relevant data of these factors are compared to eachother. The objective is to provide both perspectives with sufficient arguments to defend their statement. The insights provided in this project may help determine whether education is the only factor contributing to life expectancy.
It is important to note the pace at which life expectancy has skyrocketed over the past decades. The extraordinary rise is attributed to a wide range of advances in human development. At the start of the nineteenth century, no region had a life expectancy higher than 40 years. Nowadays, multiple countries are close to hitting 80 years, according to ourworldindata. This rapid increase in life expectancy can be visualized using a box plot.
%run life_expectancy_boxplot.ipynb
Year
1950 257
1987 257
2004 257
2003 257
2002 257
2001 257
2000 257
1999 257
1998 257
1997 257
1995 257
1994 257
1993 257
1992 257
1991 257
1990 257
1989 257
2005 257
2006 257
2007 257
2016 257
1951 257
2021 257
2020 257
2019 257
2018 257
2017 257
2015 257
2008 257
2014 257
2013 257
2012 257
2011 257
2010 257
2009 257
1988 257
1996 257
1986 257
1968 257
1966 257
1965 257
1964 257
1963 257
1962 257
1961 257
1960 257
1985 257
1958 257
1957 257
1956 257
1955 257
1954 257
1953 257
1952 257
1967 257
1959 257
1969 257
1970 257
1983 257
1982 257
1981 257
1980 257
1979 257
1978 257
1977 257
1976 257
1975 257
1974 257
1973 257
1972 257
1971 257
1984 257
1940 46
1930 45
1900 43
1920 36
1937 35
1947 33
1910 33
1935 32
1945 32
1949 32
1948 31
1931 30
1927 30
1941 30
1942 30
1946 30
1925 30
1921 29
1932 28
1926 28
1938 27
1943 27
1923 27
1922 26
1928 26
1933 26
1944 26
1939 25
1905 25
1934 25
1936 25
1913 25
1924 24
1929 24
1911 24
1915 22
1901 22
1908 21
1902 20
1895 20
1918 19
1885 19
1907 19
1909 18
1912 18
1897 18
1906 18
1896 18
1903 18
1917 17
1904 17
1875 17
1919 17
1890 17
1881 17
1891 17
1892 16
1880 16
1882 16
1899 16
1916 16
1914 16
1870 16
1886 15
1894 15
1887 15
1898 15
1893 15
1889 14
1888 14
1884 14
1883 14
1876 13
1879 13
1878 13
1877 13
1873 12
1850 12
1865 12
1871 11
1872 11
1874 11
1868 11
1855 11
1861 11
1869 10
1859 10
1851 10
1856 10
1857 10
1858 10
1860 10
1862 10
1863 10
1864 10
1866 10
1867 10
1854 9
1853 9
1852 9
1846 8
1845 8
1849 8
1847 8
1848 8
1844 7
1843 7
1842 7
1841 7
1770 6
1838 5
1835 4
1839 4
1840 4
1820 4
1823 3
1800 3
1818 3
1825 3
1775 3
1830 3
1837 3
1831 3
1833 3
1836 3
1828 3
1783 2
1788 2
1778 2
1808 2
1793 2
1798 2
1768 2
1763 2
1803 2
1758 2
1753 2
1834 2
1773 2
1832 2
1813 2
1765 2
1815 2
1829 2
1816 2
1795 2
1817 2
1819 2
1785 2
1821 2
1822 2
1805 2
1827 2
1824 2
1755 2
1826 2
1573 1
1603 1
1623 1
1618 1
1543 1
1613 1
1608 1
1548 1
1598 1
1568 1
1553 1
1558 1
1593 1
1628 1
1583 1
1578 1
1563 1
1588 1
1814 1
1633 1
1698 1
1743 1
1738 1
1733 1
1728 1
1723 1
1718 1
1713 1
1708 1
1703 1
1693 1
1638 1
1688 1
1683 1
1678 1
1673 1
1668 1
1663 1
1658 1
1653 1
1643 1
1648 1
1772 1
1812 1
1764 1
1779 1
1777 1
1776 1
1774 1
1771 1
1769 1
1767 1
1766 1
1762 1
1811 1
1761 1
1760 1
1759 1
1757 1
1756 1
1754 1
1752 1
1751 1
1780 1
1781 1
1782 1
1784 1
1810 1
1809 1
1807 1
1806 1
1804 1
1802 1
1801 1
1799 1
1797 1
1796 1
1794 1
1792 1
1791 1
1790 1
1789 1
1787 1
1786 1
1748 1
Name: count, dtype: int64
Only education and GDP have an impact on life expectancy#
As seen in the dataset, it’s very common for countries with a good education to also have a high life expectancy. To make it more clear, the data can be visualized in this Bivariate Choropleth:
In this image, the left side of the legend is the education level, and the right side is the life expectancy. As shown, almost all countries with good education quality also have a high life expectancy. The reasoning behind this might be that people with better education tend to choose for a healthier way of life. It can also be visualized in the following way. This plot shows the rate in which people finish primary and secondary school, compared to the life expectancy of said person. This graph makes clear that people with better education tend to have a higher life expectancy. A reason for this increase in life expectancy comes from the fact that people with a better education make better choices. (Raghupathi & Raghupathi, 2020b)
Education level#
Assuming education is the only factor that predicts life expectancy in a country, a closer assessment is needed to determine which sector should be invested in.
Second plot#
GDP Argument (Second argument)#
We can also argue that a society with a good education will produce an increasing GDP. Research at the university of Munich has shown that people with a better education are able to achieve jobs with more complex skill sets, resulting in a higher paying job. If people in a society are able to keep higher paying jobs, the GDP from the country of origin will increase. This in turn will influence the life expectancy of a country. Research originating from the University of Zagreb has shown that an increase in GDP of a country, also has a positive influence on the country’s life expectancy. This is confirmed when you convert the data into a Bivariate Chropleth or a scatter plot (with a regression). These charts show the GDP of a country and the country’s life expectancy. This means that the increase in education gives an increase in GDP which delivers an increase in life expectancy.
Life expectancy cannot be predicted by just education#
Even though A country investing in their education program results in an increase in life expectancy. There are more direct approaches to increasing a country’s life expectancy. One possible solution is investing in increasing the country’s vaccination rate. Diseases or viruses like Polio and Diphtheria can be fatal if not treated appropriately, in some cases (like for polio) there is no cure at all. Not treating these diseases results in a drastic decrease in life expectancy. So instead of investing in education to improve life expectancy, a country should invest in vaccines as this has a more direct effect. This can be seen in the plot where it shows an increase in vaccination rate for polio and Diphtheria corresponds with an increase in life expectancy. This is also found in the research by Jenifer Ehreth. Which concludes that improving the vaccination rate is a big factor in increasing a country’s life expectancy. https://www.sciencedirect.com/science/article/pii/S0264410X03003773
Unhealthy lifestyles#
The prevelance of unhealthy lifestyles in (developed) countries may also contribute to life expectancy.
Counter argument 2#
Another way to increase life expectancy is to invest in cleaner and safer drinking water. Unsafe drinking water is the cause of a lot of different diseases, all of which can cause a person to live a shorter life. It can be seen in the graph that an increase in the amount of people that drink from a safe water source correlates with an increase in life expectancy, this also supported by the following research paper, Angelakis et al. (2021b). This means that it should be useful for a country to invest in a clean water source before it starts to invest in different areas.
The impact of vaccination#
Another factor to consider is
Conclusion#
hier moet nog een conclusie komen
References#
Raghupathi, V., & Raghupathi, W. (2020). The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015. Archives Of Public Health, 78(1). https://doi.org/10.1186/s13690-020-00402-5
Ehreth, J. (2003). The value of vaccination: a global perspective. Vaccine, 21(27–30), 4105–4117. https://doi.org/10.1016/s0264-410x(03)00377-3
Angelakis, A. N., Vuorinen, H. S., Nikolaidis, C., Juuti, P. S., Katko, T. S., Juuti, R. P., Zhang, J., & Samonis, G. (2021). Water Quality and Life Expectancy: Parallel Courses in Time. Water, 13(6), 752. https://doi.org/10.3390/w13060752